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This study aims to provide empirical evidence to verify the dimensional structure of artificial intelligence (AI) Chatbot quality and examine the impact of these dimensions on consumer satisfaction and brand advocacy among Gen Z in the fast food industry in Egypt.

The empirical data was obtained with an electronic self-administered survey instrument from 397 young consumers who had prior experience using AI Chatbots across multiple fast food brands in Egypt. Structural equation modeling was used to analyze the formulated hypotheses.

The results showed that AI Chatbot quality dimensions, specifically information authenticity and system compliance, significantly enhance young consumers’ satisfaction. In addition, information authenticity of AI Chatbot quality was observed to wield a significant influence on young consumers’ advocacy. In contrast, an insignificant relationship was noticed between satisfaction and advocacy. Moreover, the mediating role of consumer satisfaction was not established.

Given that Gen Z is more technology savvy and computer literate, marketers and practitioners of fast food brands should invest in AI tools to respond to young consumers’ expectations and improve their perception of their services.

This study uses stimulus-organism-response theory to understand the mediating effect of young consumers’ satisfaction in the relationship between AI Chatbot quality and consumer brand advocacy within the fast food industry. Also, it introduced two novel main constructs of AI Chatbot quality, namely, information authenticity and system compliance.

Providing high-quality service for airline companies cannot be overstated, as it directly impacts their survival and competitiveness. Hence, there is an increasing need to measure loyalty in the air transport industry. Loyal customers are highly valued as they are less price-sensitive and require minimal communication efforts. Despite this, there is limited knowledge about the factors that influence a passenger’s level of loyalty towards an airline company. Therefore, understanding the precursors of loyalty is essential. This research investigates the serial mediation effect of consumer brand identification (CBI) and perceived value on the relationship between service quality and loyalty.

In this research, quality is related to loyalty through value and CBI. A conclusive research design was adopted to determine whether value and CBI have a serial mediating effect on the quality-loyalty link. The research framework includes a three-path mediation model.

Based on the analysis of 406 questionnaires, the study concludes that a serial mediation effect of CBI and perceived value on the quality-loyalty relationship exists. The research results indicate that CBI has a greater influence on improving customer loyalty than perceived value.

This study contributes to limited research on the serial mediating effect of value and CBI in the quality-loyalty relationship. It also provides empirical evidence on the determinants of loyalty as part of the Turkish aviation industry. This study highlights the significance of CBI as a critical factor for airlines to maintain their competitiveness in the market.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation.

Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation.

Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control.

The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece.

This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry.

This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.

This study aims to improve the force sensing performance of the robot joint for the safety and flexibility of physical human–robot interaction.

A force sensing mechanism (FSM) for an S-shaped spring of a robot variable stiffness actuator (VSA) was designed. The yield strength of the spring material, geometric and assembly structure constraints of the VSA are all considered for the actuator deflection limit design. The elastic deformation model is solved in reverse to obtain the local deformation limit profile of the S-spring at different spring angles. The deformation limit mechanism is manufactured by three-dimensional printing and assembled with S-springs. The force sensing function for the VSA is achieved by the input and output shaft encoders and stiffness model. The FSM is verified by torque-deflection experiments with variable stiffness.

The yield strength of the S-spring material is the strictest constraint for elastic deformation. Experimental results show that the external force can be quickly and reliably perceived. As the spring angle increases (stiffness increases), the hysteresis and nonlinear error decrease. Under the constraint of the FSM, the maximum deflection also decreases rapidly.

The designed FSM based on the deformation and stiffness model provides a comprehensive design reference in a VSA with nonlinear elastic mechanisms, which is ignored but important for exploring the VSAs potential.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

With the accumulation of theoretical research and practical experience in the field of garment production research, it is imperative to methodically analyze and reflect on the achievements that have been made. This review aims to systematically map the academic landscape of research articles on garment production, elucidate the evolutionary trajectory of this discipline, identify emerging research frontiers and provide insights into its prospects.

Based on the Web of Science core database, 307 research articles were systematically analyzed by CiteSpace software. The study employed bibliometric and thematic analyses to offer in-depth insights into the dynamics and evolution of research on garment production.

Results reveal that keyword analysis emphasizes the significance of topics such as apparel assembly line, lean production, circular economy, fuzzy logic, global production networks, social sustainability and supply chain management in garment production research. Citation analysis demonstrates that articles related to environmental impact, supply chain management, production process and production technology constitute the knowledge base and core of garment production research. Eight principal research themes emerge: customized garment production, production technology, quality assurance, equipment, production lines, supply chain management, environmental impact and social and human impact. Future research hotspots will focus more on sustainable, intelligent and digital clothing production.

The findings systematically sort out the hotspots and trends in garment production, establish knowledge structures and display them through intuitive representations. The rich insights set the stage for the development of garment production and provide future guidance for theoretical research.

Rubber-plastic double-layer bush water-lubricated bearings have demonstrated superior performance, while research on their vibration characteristics remains limited. This paper aims to investigate the lubrication and vibration properties of these bearings by experiments and examine the effect of rubber-to-plastic bush thickness ratio on bearing performance.

A water-lubricated journal bearing test rig is constructed, and three bearings with different bush thickness ratios are fabricated. Bush deformation under various loads is measured, and the friction coefficient and axis trajectory under different operating conditions are tested. The vibration responses of the bearings under directional harmonic excitation are studied. The influences of rotational speed, load and rubber-to-plastic bush thickness ratio on the bearing’s lubrication and vibration properties are analyzed.

The friction coefficient of the bearing initially decreases rapidly and subsequently increases gradually as the rotational speed or load increases. The bearing with a thicker rubber bush shows lower displacement amplitudes in its axis trajectory. Under a 45° directed excitation, significant oscillations are observed in the vertical displacement, while the horizontal displacement remains stable. The damping effect of the bearing with a thicker rubber bush is more pronounced.

This paper present the influence of rubber-to-plastic bush thickness ratio on bearing lubrication and vibration performance. The results are valuable for the design of this type of bearing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0469/

Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors.

This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness.

Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers.

This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.